Abstract and Applied Analysis

Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

Gai-Ge Wang, Lihong Guo, Amir Hossein Gandomi, Amir Hossein Alavi, and Hong Duan

Full-text: Open access

Abstract

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2012), Article ID 213853, 11 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393450426

Digital Object Identifier
doi:10.1155/2013/213853

Mathematical Reviews number (MathSciNet)
MR3070193

Zentralblatt MATH identifier
1291.90330

Citation

Wang, Gai-Ge; Guo, Lihong; Gandomi, Amir Hossein; Alavi, Amir Hossein; Duan, Hong. Simulated Annealing-Based Krill Herd Algorithm for Global Optimization. Abstr. Appl. Anal. 2013, Special Issue (2012), Article ID 213853, 11 pages. doi:10.1155/2013/213853. https://projecteuclid.org/euclid.aaa/1393450426


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